Host biomarkers for monitoring therapeutic response in extrapulmonary tuberculosis

Purpose The aim of this study was to explore the utility of inflammatory biomarkers in the peripheral blood to predict response to treatment in extrapulmonary tuberculosis (EPTB). Methods A Luminex xMAP-based multiplex immunoassay was used to measure 40 inflammatory biomarkers in un-stimulated plasma of 91 EPTB patients (48 lymphadenitis, and 43 pleuritis) before and at 2 and 6 months of treatment. Results Overall a significant change was observed in 28 inflammatory biomarkers with treatment in EPTB patients. However, MIG/CXCL9, IP-10/CXCL10, and CCL23 decreased in all patients' groups with successful treatment at both time points. At 2 months, 29/64 (45%) patients responded partially while 35/64 (55%) showed complete regress. Among good responders, a higher number of biomarkers (16/40) reduced significantly as compared to partial responders (1/40). Almost half (14/29) of partial responders required longer treatment than 6 months to achieve satisfactory response. The levels of MIG, IP-10, MIF, CCL22 and CCL23 reduced significantly among 80, 74, 60, 71, 51% good responders, as compared to 52, 52, 52, 59, 52% partial responders, respectively. A biosignature, defined by a significant decrease in any one of these five biomarkers, corresponded with satisfactory response to treatment in 97% patients at 2 month and 99% patients at 6 months of treatment. Conclusion Change in inflammatory biomarkers correlates with treatment success. A five biomarker biosignature (MIG, IP-10, MIF, CCL22 and CCL23) could be used as an indicator of treatment success.publishedVersio

Seasonality and trend analysis of tuberculosis in Lahore, Pakistan from 2006 to 2013

Tuberculosis (TB) is a respiratory infectious disease which shows seasonality. Seasonal variation in TB notifications has been reported in different regions, suggesting that various geographic and demographic factors are involved in seasonality. The study was designed to find out the temporal and seasonal pattern of TB incidence in Lahore, Pakistan from 2006 to 2013 in newly diagnosed pulmonary TB cases. SPSS version 21 software was used for correlation to determine the temporal relationship and time series analysis for seasonal variation. Temperature was found to be significantly associated with TB incidence at the 0.01 level with p = 0.006 and r = 0.477. Autocorrelation function and partial autocorrelation function showed a significant peak at lag 4 suggesting a seasonal component of the TB series. Seasonal adjusted factor showed peak seasonal variation in the second quarter (April–June). The expert modeler predicted the Holt–Winter’s additive model as the best fit model for the time series, which exhibits a linear trend with constant (additive) seasonal variations, and the stationary R2 value was found to be 0.693. The forecast shows a declining trend with seasonality. A significant temporal relation with a seasonal pattern and declining trend with variable amplitudes of fluctuation was observed in the incidence of TB

Statistical data for the release of IFN-γ (pg/mL) by PBMCs from the TB and healthy subjects against the single antigens and their fusion constructs.

Statistical data for the release of IFN-γ (pg/mL) by PBMCs from the TB and healthy subjects against the single antigens and their fusion constructs.</p

T-cell specific epitopes of the antigens used in this study as the scheme for construction of the fusion molecules.

T-cell specific epitopes of the antigens used in this study as the scheme for construction of the fusion molecules.</p

S1 Raw images -

(PDF)</p

Structural analyses of the fusion antigens bifu25, trifu37, trifu44 and tetrafu56.

T-cell epitopes in the 3D molecular structures (i-iv) are shown in black. CPORT analysis (a-d) shows the epitope regions as red, green or blue color, representing active, supporting or non-supporting residues, respectively for cellular interaction.</p

Th1-cell epitopes predicted for the <i>Mtb</i> antigens.

Th1-cell epitopes predicted for the Mtb antigens.</p

Fig 5 -

IFN-ℽ release (a) and Pearson’s correlation values (b) between IFN-ℽ released against mixtures of the antigens and their fusions. IFN-ℽ release (pg/mL) from PBMCs of active TB patients against mixtures of the antigens (red) and their fusions (green) in Pearson correlation graphs (b).</p

SDS-PAGE showing expression of bifu25 and bifu29 and the other fusion proteins.

Lanes M: Protein markers; 1: Uninduced E. coli cells; 2: Lysate of the induced E. coli cells; 3: Soluble fraction of the cell lysate; 4: Insoluble fraction of the cell lysate.</p